Image compression is essential to the successful deployment of digital medical imaging systems, also referred to as Picture Archive and Communications Systems (PACS). Image compression is a processing operation that reduces the amount of digital data required to represent an image, and hence, enables more efficient utilization of both available network bandwidth and image archive storage space, thereby reducing the cost to implement PACS. There are fundamentally two types of image compression. The first type is known as lossless compression. With lossless compression the reconstructed image identically matches the original and is therefore a fully reversible process. Since the diagnostic quality of the compressed and reconstructed image is assured relative to the original image, lossless compression is appealing for medical applications. The major drawback of lossless compression is the limited compression ratios that can be achieved, which is typically on the order of 2:1, or equivalently a 50% reduction in file size. Lossy methods can achieve much greater compression ratios, on the order of 10:1 or higher, and can therefore provide more significant cost savings for PACS implementations. However the medical community has been slow to accept lossy compression for fear that important diagnostic information could be lost. It is therefore necessary to provide a means of achieving higher compression ratios than those achievable with lossless methods but in a manner that will be widely acceptable to the medical imaging community. Such a method would provide reasonable cost savings for PACS and minimize the potential for loss of important diagnostic information.
Reid et al, "Second-generation image coding: an overview," ACM Computing Surveys, Vol. 29, No. 1, pp. 2-29, March 1997, in overviewed recently-developed image compression techniques that have been termed second-generation image coding. These methods incorporate properties of the human visual system into the coding strategy in order to achieve high compression ratios while maintaining acceptable image quality. The techniques utilized in second-generation image coding are based on visual patterns, multi-scale decomposition, contour-coding, and segmentation. Visual-pattern based approaches use the fact that the eye can decompose the overall image into a set of smaller "visual patterns". Multi-scale decomposition techniques create sets of progressively smaller images and identify common features in the image that are present at the various levels of detail. All second-generation coding techniques are lossy in nature. However, these methods attempt to identify and separate visually significant and visually insignificant areas of the image, and apply appropriate coding techniques to each area.
Researchers in W. C. Chang, et al, "Lossless image compression methods for PET imaging," Biomedical Engineering-Applications, Basis & Communications, Vol. 8, No. 3, pp. 309-316, June 1996; L. Shen et al., "Segmentation-based lossless coding of medical images," Proceedings of the SPIE-The International Society for Optical Engineering Conference, 24-26 May 1995, Taipei, Taiwan, SPIE, Vol. 2501, pp. 974-982; and V. Vlahakis et al., "ROI approach to wavelet-based, hybrid compression of MR images," Proceedings of 6.sup.th International Conference on Image Processing and its Applications, 14-17 Jul. 1997, Dublin, Ireland, Part Vol. 2, pp. 833-837 proposed segmentation-based hybrid lossless image compression coding methods for medical images. W. C. Chang et al, "Lossless image compression methods for PET imaging," Biomedical Engineering-Applications, Basis & Communications, Vol. 8, No. 3, pp. 309-316, June 1996, described a hybrid lossless coding method for PET (Positron Emission Tomography) images. The supported region (cross-section region) and unsupported region (background region) was separated by a binary mask using a thresholding segmentation algorithm. The unsupported region was not encoded while the supported region was encoded using a lossless entropy coding method. However, the boundary of the binary mask, which is the contour of the segmented supported region, had to be encoded using the chain code method. Extra bytes for describing the shape of the contour also needed to be provided to both encoder and decoder in order to reconstruct the image.
Another segmentation-based lossless coding method was applied to digitized mammography and chest radiography film images in L. Shen et al., "Segmentation-based lossless coding of medical images," Proceedings of the SPIE--The International Society for Optical Engineering Conference, 24-26 May 1995, Taipei, Taiwan, SPIE Vol. 2501, pp. 974-982. The region growing scheme was used to generate segments at which gradients of gray levels were within certain thresholds. The discontinuity index map data sets were also generated to present the pixels which separated segments. An entropy coding method was applied to code segments individually. However, it was necessary to send extra data with the compressed image to correctly index the segments for decompression.
One region of interest (ROI) approach to a wavelet-based hybrid compression method for magnetic resonance (MR) images was proposed in V. Vlahakis, et al., "ROI approach to wavelet-based, hybrid compression of MR images," Proceedings of 6.sup.th International Conference on Image Processing and its Applications, 14-17 July 1997, Dublin, Ireland, Part Vol. 2, pp. 833-837. A MR image was decomposed using a wavelet transform into three scales. Starting at the middle scale, scale 2, the radiologist clicked the mouse at a seed pixel inside the area which he identified as the ROI (corresponding to brain tissue, tumors, etc.), and a seed-fill segmentation algorithm scanned and labeled the pixels around it until a boundary was detected. Then fine or no quantization was used for wavelet coefficients corresponding to ROI, and coarse quantization for the rest of the coefficients of scale 1 and 2. Finally, the quantized coefficients were run-length coded and the resulting run-lengths were compressed with a Huffman code. The low-pass residue of scale 3 was losslessly compressed using DPCM (Differential Pulse Code Modulation).
Other hybrid lossy and lossless compression methods for consumer images are disclosed in the following U.S. patents. U.S. Pat. No. 5,552,898, issued Sep. 3, 1996, to inventor F. A. Deschuytere teaches a method of lossy and lossless compression in a raster image processor on output devices. Digital input commands defined in a page description language are separated into two types of instructions. The first set of instructions comprise solid regions on the printed output, which are filled with recorder elements (e.g., ink) of the same highest or lowest density value, and second instructions resulting in halftoned regions, which are getting different densities. It is advantageous to distinguish a first type of instructions from a second type of instructions, and treat them separately. The information stored in the first type of instructions is compressed by a lossless compression method (recommended by CCITT--International Telegraph and Telephone Consultative Committee). As such, solid patterns will appear on the rasterized image at the highest resolution and without any quality loss. The second type of instructions on the other hand corresponds to continuous tone image or intermediate tone graphical information. A slight deterioration of the information contents is acceptable and will be hardly noticeable, thus is compressed by a lossy compression method (JPEG). When all the digital input commands for one page are handled, the compressed data can be retrieved and combined to reconstruct the rasterized image.
U.S. Pat. No. 5,553,160, issued Sep. 3, 1996, to inventor B. J. Dawson teaches a method and apparatus for dynamically selecting an image compression process based on image size and color resolution to be transferred from a first agent to a second agent. For example, if the image size is less than 4K bytes, the image remains uncompressed. If the image size is greater than 4K bytes and color resolution is less than 8 bits, the image is compressed using a lossless compression process. If the image size is greater than 4K bytes and color resolution is greater that 8 bits, then the lossless process is run on a predetermined portion of the image. This predetermined portion could be the first 10K bytes, the last 10K bytes or the middle 10K bytes in the image. After running the lossless process in this step, if the compression ratio for the predetermined portion is greater than or equal to 5:1, then a lossy process is used for compression, otherwise a couple of more decisions are made where lossless or lossy compression method is used.
Another method treats binary text images using lossy and lossless compression to achieve high compression ratio is disclosed in U.S. Pat. No. 5,204,756, issued Apr. 20, 1993, to inventors D. S. Chevion et al. The method uses variable compression ratios which depend on an evaluation of the nature of the binary image at hand, with lossy compression being limited to large low-frequency areas where quality deterioration is subjectively unnoticeable, and with small high-frequency areas being compressed losslessly.
The present invention differs from prior art in that reconstruction errors are introduced into the compression process in a controlled manner based on the inherent noise properties of the image acquisition system. The present invention discloses a method of statistically lossless compression based on the preservation of the image statistics in the local neighborhood around each image pixel. Prior art teaches image segmentation-based compression wherein images are adaptively quantized in order to minimize the perception of distortion based on psycho-visual criteria. The distortion introduced by the method disclosed in the present invention does not depend on any psycho-visual based rules or criteria. The present invention combines four steps to achieve virtually lossless compression. First, the image is segmented into two regions, the foreground and the region-of-interest (ROI). Second, the foreground region is replaced by a field of uniform code values (typically a black) so that this region has zero pixel modulation. Third, a look-up-table (LUT) is applied to the image data to reduce the number of bits per pixel required to represent the image without introducing quantization into the image. This step is known as companding. The companding function is derived from the inherent noise properties of the image acquisition system using a theory which describes the modification of statistical moments in images due to quantization. Finally, the image is compressed using a new standard for lossless image compression known as JPEG-LS (ISO/IED JTC 1/SC 29/WG 1 FCD 14495--public draft, FCD 14495, Lossless and near-lossless coding of continuous tone still images (JPEG-LS)). In addition to the standard lossless mode of compression, JPEG-LS also allows the user to trade constrained reconstruction errors for increased amounts of compression. The companding step may be substituted, albeit at the expense of some loss in compression efficiency, in favor of the reconstruction error tolerance approach to maintain compliance with the JPEG-LS standard. Either method can be implemented to satisfy the statistically lossless criterion.
The following are representative of the prior art.
U.S. Pat. No. 5,552,898, September 1996, F. A. Deschuytere, "Lossy and Lossless compression in raster image processor."
U.S. Pat. No. 5,553,160, September 1996, B. J. Dawson, Method and apparatus for dynamically selecting an image compression process based on image size and color resolution."
U.S. Pat. No. 5,204,756, April 1993, D. S. Chevion, E. D. Karnin and E. Walach, "Method for high-quality compression of binary text images."
U.S. Pat. No. 5,633,511, May 1997, H. C. Lee, L. L. Lori and R. A. Senn, "Automatic tone scale adjustment using image activity measures."